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The chemometric philosophy is based on some well-defined and shared principles: multivariate analysis of the problem, searching for the relevant information of data, model validation to produce predictive models, definition of the model applicability domain for reliable predictions and graphical visualization of the relevant information. The verification of food authenticity is fundamentally a classification problem; indeed, samples are labeled according to their authenticity status, that is, authentic or non-authentic. Chemometric methods are usually applied to data that are collected in a numerical table. Chemometric methods usually require a pre-treatment of the raw data in order to be able to extract useful and non-trivial information and discard obvious (i.e., intrinsic in the single variable) and useless information. Soft independent modeling of class analogy is one of the most popular class modeling approaches. Artificial Neural Networks are currently considered among the most important emerging tools in chemometrics.

In this study, we propose a new approach based on Raman hyperspectral imaging and chemometrics for the analysis of chemically heterogeneous surfaces of weathered minerals. A pyrite sample showing a heterogeneous surface with different alteration products, is used to test the validity of the technique. Principal Component Analysis (PCA) was initially used to evaluate data structure and identify Raman phases and vibrational modes related to minor and major weathering features. Afterwards, Multivariate Curve Resolution-alternating least squares (MCR-ALS) was carried out to identify the specific chemical components of the major weathering phases. Finally, for the analysis of the minor weathering phases, K-means clustering was carried out to identify groups of similar pixels of low percentage of variance in the image. Three major components are found in the analyzed sample, corresponding to pyrite and hematite vibrational modes, as well as features of an amorphous alteration product patina. Four additional spectral signatures revealing the presence of sulfates were found after the analysis of minor components. The proposed method enables a semi-quantitative threshold-based characterization of chemical features and provides a visual representation of the phase distribution on the surface of the sample.

According to the 2021 World Drug Report, around 275 million people use drugs of abuse, and 36 million people suffer from addiction, fostering a thriving market for illicit substances. In Italy, 30,083 people were reported to the Judicial Authority for offenses in violation of the Italian Law D.P.R. 309/1990. These offences are sentenced after a qualitative–quantitative analysis of seized materials. Given the large quantity of seized drugs and the need to perform accurate analytical determinations, Italian forensic laboratories struggle to complete analyses in a short time, delaying the entire reporting process needed to achieve sentencing. For this purpose, an UHPLC-MS/MS-based platform was developed at the University of Milano-Bicocca to support law-enforcement authorities. Software was designed to easily manage street seizure acquisition, documentation registration, and sampling. A sensitive UHPLC-MS/MS method was fully validated for the quantification of the traditional illicit substances (cocaine, heroin, 6-MAM, morphine, amphetamine, methamphetamine, MDMA, ketamine, GHB, GBL, LSD, trans-∆9-THC, and THCA) at the ppb level. The final report is relayed to the Prefecture in 3–4 days, even within 24 h for urgent requests. The platform allows for semi-automatic data handling to minimize erroneous results for an accurate report generation by standardized procedures.

Membrane introduction mass spectrometry (MIMS) is a direct mass spectrometry technique used to monitor online chemical systems or quickly quantify trace levels of different groups of compounds in complex matrices without extensive sample preparation steps and chromatographic separation. MIMS utilizes a thin, semi-permeable, and selective membrane that directly connects the sample and the mass spectrometer. The analytes in the sample are pre-concentrated by the membrane depending on their physicochemical properties and directly transferred, using different acceptor phases (gas, liquid or vacuum) to the mass spectrometer. Condensed phase (CP) MIMS use a liquid as a medium, extending the range to new applications to less-volatile compounds that are challenging or unsuitable to gas-phase MIMS. It directly allows the rapid quantification of selected compounds in complex matrices, the online monitoring of chemical reactions (in real-time), as well as in situ measurements. CP-MIMS has expanded beyond the measurement of several organic compounds because of the use of different types of liquid acceptor phases, geometries, dimensions, and mass spectrometers. This review surveys advancements of CP-MIMS and its applications to several molecules and matrices over the past 15 years.

Antineoplastic drugs are a wide and heterogeneous group of substances that, as universally known, can cause highly severe toxic effects to whoever is exposed. From an occupational safety point of view, surface contaminations inside preparation and administration units are a growing issue and therefore require the development and implementation of sensible and fast monitoring methods. The unlikelihood of a unique all-embracing chromatography, able to correctly retain and separate each analyte led to the need to create an orthogonal normal phase analysis, which might be able to fill the gaps in the more common reversed-phase ones. An existing hydrophilic interaction method has thus been expanded to 6 other drugs and applied to real samples after an evaluation of its performances. The experimental data were then used to evaluate the possibility of estimating reliable relationships between the chromatographic retention and the chemical-structural features of the drugs under analysis.

Mass spectrometry (MS) is widely used for the identification of chemical compounds by matching the experimentally acquired mass spectrum against a database of reference spectra. However, this approach suffers from a limited coverage of the existing databases causing a failure in the identification of a compound not present in the database. Among the computational approaches for mining metabolite structures based on MS data, one option is to predict molecular fingerprints from the mass spectra by means of chemometric strategies and then use them to screen compound libraries. This can be carried out by calibrating multi-task artificial neural networks from large datasets of mass spectra, used as inputs, and molecular fingerprints as outputs. In this study, we prepared a large LC-MS/MS dataset from an on-line open repository. These data were used to train and evaluate deep-learning-based approaches to predict molecular fingerprints and retrieve the structure of unknown compounds from their LC-MS/MS spectra. Effects of data sparseness and the impact of different strategies of data curing and dimensionality reduction on the output accuracy have been evaluated. Moreover, extensive diagnostics have been carried out to evaluate modelling advantages and drawbacks as a function of the explored chemical space.

The study concerns the photodegradation of the antidepressant escitalopram (ESC), the S-enantiomer of the citalopram raceme, both in ultrapure and surface water, considering the contribution of indirect photolysis through the presence of nitrate and bicarbonate. The effect of nitrate and bicarbonate concentrations was investigated by full factorial design, and only the nitrate concentration resulted in having a significant effect on the degradation. The kinetics of ESC photodegradation is the pseudo-first-order (half-life = 62.4 h in ultrapure water and 48.4 h in lake water). The generation of transformation products (TPs) was monitored through a developed and validated HPLC-MS/MS method. Fourteen TPs were identified in ultrapure water (one of them, at m/z 261, for the first time) and other two TPs at m/z 327 (found for the first time in this study) were identified only in presence of a nitrate. Several TPs were the same as those formed during the photodegradation of citalopram. The photodegradation pathway of ESC and its mechanism of degradation in water is proposed. The method was applied successfully to the analyses of surface water samples, in which a few dozen of ng L−1 of ESC was determined together with the presence of TP2, TP5 and TP12. Finally, a preliminary in silico evaluation of the toxicological profile and environmental behavior of TPs by computational models was carried out; two TPs (TP4 and TP10) were identified as of potential concern, as they were predicted mutagenic by Ames test model.

Bitter pit (BP) is one of the most relevant post-harvest disorders for apple industry worldwide, which is often related to calcium (Ca) deficiency at the calyx end of the fruit. Its occurrence takes place along with an imbalance with other minerals, such as potassium (K). Although the K/Ca ratio is considered a valuable indicator of BP, a high variability in the levels of these elements occurs within the fruit, between fruits of the same plant, and between plants and orchards. Prediction systems based on the content of elements in fruit have a high variability because they are determined in samples composed of various fruits. With X-ray fluorescence (XRF) spectrometry, it is possible to characterize non-destructively the signal intensity for several mineral elements at a given position in individual fruit and thus, the complete signal of the mineral composition can be used to perform a predictive model to determine the incidence of bitter pit. Therefore, it was hypothesized that using a multivariate modeling approach, other elements beyond the K and Ca could be found that could improve the current clutter prediction capability. Two studies were carried out: on the first one an experiment was conducted to determine the K/Ca and the whole spectrum using XRF of a balanced sample of affected and non-affected ‘Granny Smith’ apples. On the second study apples of three cultivars (‘Granny Smith’, ‘Brookfield’ and ‘Fuji’), were harvested from two commercial orchards to evaluate the use of XRF to predict BP. With data from the first study a multivariate classification system was trained (balanced database of healthy and BP fruit, consisting in 176 from each group) and then the model was applied on the second study to fruit from two orchards with a history of BP. Results show that when dimensionality reduction was performed on the XRF spectra (1.5 - 8 KeV) of ‘Granny Smith’ apples, comparing fruit with and without BP, along with K and Ca, four other elements (i.e., Cl, Si, P